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A multi-scale robotic tool grasping method for robot state segmentation masks.

Tao Xue1, Deshuai Zheng1, Jin Yan1

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.

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Summary
This summary is machine-generated.

This study introduces a modular system for human-robot collaboration, enabling robots to understand tool use. The system accurately identifies tools and predicts optimal grasp and handover configurations for seamless task execution.

Keywords:
grasp detectionhuman-robot collaborationinstance segmentationrobotic grasp platformrobotic grasping

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Robots increasingly collaborate with humans in shared workspaces.
  • Effective human-robot collaboration requires robots to understand tool usage and task context.

Purpose of the Study:

  • To develop a modular system for robots to better understand and participate in collaborative tasks involving tools.
  • To improve the accuracy and efficiency of robot grasp and handover predictions in human-robot interactions.

Main Methods:

  • A multi-layer instance segmentation network identifies task-related tools and classifies objects based on robot state.
  • A multi-scale grasping network (MGR-Net) predicts optimal grasp and handover configurations using state semantic region masks.
  • A novel real-world tool dataset was constructed for system evaluation.

Main Results:

  • The system accurately identifies tools and generates state semantic regions, classifying robot roles as 'leader' or 'assistant'.
  • MGR-Net demonstrated higher accuracy in predicting grasp and handover configurations compared to traditional methods.
  • The system achieved strong performance on untrained real-world tool datasets and was validated on a Sawyer robot platform.

Conclusions:

  • The proposed modular system enhances robot understanding of tool-use in collaborative tasks.
  • The MGR-Net effectively predicts grasp and handover configurations, improving human-robot interaction.
  • The system shows significant potential for real-world applications in collaborative robotics.